Rice is currently the most important food crop in the world and we are only just beginning to study the bacterial associated microbiome. It is of importance to perform screenings of the core rice microbiota and also to develop new plant-microbe models and simplified communities for increasing our understanding about the formation and function of its microbiome. In order to begin to address this aspect, we have performed a 16S rDNA taxonomic bacterial profiling of the rhizosphere and endorhizosphere of two high-yield rice cultivars—Pionero 2010 FL and DANAC SD20A—extensively grown in Venezuela in 2014. Fifteen putative bacterial endophytes were then isolated from surface-sterilized roots and further studied in vitro and in planta. We have then performed inoculation of rice seedlings with a simplified community composed by 10 of the isolates and we have tracked them in the course of 30 days in greenhouse cultivation. The results obtained suggest that a set was able to significantly colonize together the rice endorhizospheres, indicating possible cooperation and the ability to form a stable multispecies community. This approach can be useful in the development of microbial solutions for a more sustainable rice production.

Fusarium wilt of banana (Musa spp.), caused by the soil-borne fungus Fusarium oxysporum f. sp. cubense (Foc), is a major constraint to banana production worldwide (Dita et al., 2018). A strain of F...

Accurate predictions of crop production are critical to developing effective strategies at the farm level. Knowing banana production is due to the need to maximize the investment–profit ratio, and the availability of this information in advance allows decisions to be made about the management of important diseases. The objective of this study was to predict the number of banana bunches from epidemiological parameters of Black Sigatoka (BS), using random forests (RF) for its ability to predict crop production responses to epidemiological variables. Weekly production data (number of banana bunches) and epidemiological parameters of BS from three adjacent banana sites in Panama during 2015–2018 were used. RF was found to be very capable of predicting the number of banana bunches, with variance explained as 70.0% and root mean square error (RMSE) of 1107.93 ± 22 of the mean banana bunches observed in the test case. The site, week, youngest leaf spotted and youngest leaf with symptoms in plants with 10 weeks of physiological age were found to be the best predictor group. Our results show that RF is an efficient and versatile machine learning method for banana production predictions based on epidemiological parameters of BS due to its high accuracy and precision, ease of use, and usefulness in data analysis.

Tomato yellow leaf curl virus (TYLCV), a member of the family Geminiviridae, is a serious production constraint to tomato worldwide. In the new world, the virus had been identified as the causal agent of tomato yellow leaf curl disease in the Caribbean countries of the Dominican Republic, Cuba, and Guadeloupe and also in Florida and the Yucatan Peninsula (1). Molecular data from these TYLCV isolates identified the virus as the TYLCV prototype from Israel. During April 2004, tomato plants showing symptoms such as chlorotic leaf edges, upward leaf cupping, leaf mottling, and reduced leaf size indicative of TYLCV were observed in commercial fields in Zulia state, Venezuela. Whiteflies (Bemisia tabaci Gennadius) were present in the field and appeared to be associated with the disease. Leaf samples from nine symptomatic plants were collected and brought to the lab at Instituto Venezolano de Investigaciones Científicas (IVIC) for further analyses. Geminivirus infection of samples was confirmed by PCR amplification with the degenerate primer pair PAL1v1978 and PAR1c494 (2). TYLCV coat protein gene-specific primers KL04-06_TYLCV CP F and KL04-07_TYLCV CP R (3) were used to confirm the diagnosis. These primers amplified the expected 842-bp PCR product from the nine symptomatic samples. One of the resulting amplicons was cloned into the pCR-TOPO vector (Invitrogen, Carlsbad, CA) and sequenced (GenBank Accession No. DQ302033). Sequence comparison with those available in the NCBI database indicated that the sequenced portion of the genome shared 99% nucleotide identity with the TYLCV mild strain from Portugal (GenBank Accession No. AF105975) and 98% nucleotide identity with the TYLCV mild strain from Spain (GenBank Accession No. AF071228), TYLCV Israel isolate (GenBank Accession No. AM234066), and TYLCV Mexico isolate (GenBank Accession No. DQ631892). To our knowledge, this is the first report of TYLCV infecting tomato crops in South America. Further studies are needed to clarify how TYLCV has been introduced into Venezuela. References: (1) J. E. Polston and P. K. Anderson. Plant Dis. 81:1358, 1997. (2) M. R. Rojas et al. Plant Dis. 77:340, 1993. (3) K. S. Ling et al. Plant Dis. 90:379, 2006.

Yardlong bean (Vigna unguiculata subsp. sesquipedalis) plants with virus-like systemic mottling and leaf distortion were observed in both experimental and commercial fields in Aragua State, Venezuela. Symptomatic leaves were shown to contain carlavirus-like particles. RT-PCR analysis with carlavirus-specific primers was positive in all tested samples. Nucleotide sequences of the obtained amplicons showed 84%–74% similarity to corresponding sequences of Cowpea mild mottle virus (CPMMV) isolates deposited in the GenBank database. This is the first report of CPMMV in Venezuela and is thought to be the first report of CPMMV infecting yardlong bean.